rmarkdown::render(‘./1_JP_scripts/2_MBP-clustering_HARDAC.Rmd’)

Changes in myeloid and kidney cells after CLP - Analysis of 2 x 10X scRNA-seq samples from 2 pools of WT mice (3 Sham + 3 CLP): comparison of gene expression in different cell populations

indir <- "./processedData/1_JP_analyses_results/Rerun_HARDAC_20210216/1_QC_filtering_metrics"
outdir <- "./processedData/1_JP_analyses_results/Rerun_HARDAC_20210216/2_MBP-clustering"
dir.create(outdir, recursive = T)
library(Seurat)
filtered <- readRDS(paste0(indir, "/15.filtered.398.rds"))
filtered
## An object of class Seurat 
## 22399 features across 18055 samples within 1 assay 
## Active assay: RNA (22399 features, 0 variable features)

Normalize each sample individually and selected 2000 most variable genes between samples

library(cowplot)
list <- SplitObject(filtered, split.by = "sample.id")
list <- lapply(X = list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})

anchors <- FindIntegrationAnchors(object.list = list, dims = 1:20)
integrated <- IntegrateData(anchorset = anchors, dims = 1:20)
DefaultAssay(integrated) <- "integrated"
integrated <- ScaleData(integrated, verbose = T)
integrated
## An object of class Seurat 
## 24399 features across 18055 samples within 2 assays 
## Active assay: integrated (2000 features, 2000 variable features)
##  1 other assay present: RNA
DefaultAssay(integrated) <- "integrated"
integrated <- RunPCA(integrated, npcs = 30, verbose = FALSE)
integrated <- RunUMAP(integrated, reduction = "pca", dims = 1:10)
integrated <- FindNeighbors(integrated, reduction = "pca", dims = 1:10)
integrated <- FindClusters(integrated, resolution = 0.25)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 18055
## Number of edges: 616749
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9527
## Number of communities: 15
## Elapsed time: 2 seconds
d <- DimPlot(integrated, reduction = "umap", pt.size = 0.2, label = T, 
    label.size = 6)
d

pdf(paste0(outdir, "/5_DimPlot_umap_clusters_pc10_res0_25.pdf"), 
    width = 10, height = 8)
d
dev.off()
## png 
##   2
p1 <- DimPlot(integrated, reduction = "umap", group.by = "sample.id", 
    pt.size = 0.2)
p2 <- DimPlot(integrated, reduction = "umap", label = TRUE, pt.size = 0.2, 
    label.size = 6, )
plot_grid(p1, p2)

pdf(paste0(outdir, "/6_2DimPlots_umap_samples_clusters_pc10_res0_25.pdf"), 
    width = 18, height = 8)
plot_grid(p1, p2)
dev.off()
## png 
##   2
p1 <- DimPlot(integrated, reduction = "umap", group.by = "sample.id")
p2 <- DimPlot(integrated, reduction = "umap", label = TRUE)
plot_grid(p1, p2)

d <- DimPlot(integrated, reduction = "umap", group.by = "sample.id", 
    split.by = "sample.id", pt.size = 0.2, ncol = 2)
d

pdf(paste0(outdir, "/7_DimPlot_umap_split_by_samples.pdf"), width = 16, 
    height = 9)
d
dev.off()
## png 
##   2
f <- FeaturePlot(integrated, features = c("Nphs2", "Slc5a2", 
    "Clcnka", "Slc12a1", "Ptgs2", "Slc12a3", "Calb1", "Aqp2", 
    "Slc4a1", "Slc26a4", "Slc14a2", "Upk1a", "Cd22", "Adgre1", 
    "Pecam1", "Pdgfrb", "Cd68", "Cd14", "Acta2", "Csf3r", "Cd4"), 
    min.cutoff = "q9")
f

pdf(paste0(outdir, "/8_FeaturePlot_cellID.pdf"), width = 10, 
    height = 8)
f
dev.off()
## png 
##   2

##Annotation of markers based on cluster markers from Susztak Science paper (Park et al., Science 360, 758–763 (2018) and Kidney International (2019) 95, 787–796; https://doi.org/10.1016/

#Podocyte markers

f2 <- FeaturePlot(integrated, features = c("Nphs2", "Podxl"), 
    min.cutoff = "q9")
f2

pdf(paste0(outdir, "/9_FeaturePlot_Podo.pdf"), width = 10, height = 8)
f2
dev.off()
## png 
##   2

#Endothelial markers

f3 <- FeaturePlot(integrated, features = c("Plat", "Pecam1"), 
    min.cutoff = "q9")
f3

pdf(paste0(outdir, "/10_FeaturePlot_Endo.pdf"), width = 10, height = 8)
f3
dev.off()
## png 
##   2

#PT-S1 markers

f4 <- FeaturePlot(integrated, features = c("Slc5a2", "Slc5a12"), 
    min.cutoff = "q9")
f4

pdf(paste0(outdir, "/11_FeaturePlot_PTs1.pdf"), width = 10, height = 8)
f4
dev.off()
## png 
##   2

#PT-S2 markers

f5 <- FeaturePlot(integrated, features = c("Fxyd2", "Hrsp12"), 
    min.cutoff = "q9")
f5

pdf(paste0(outdir, "/12_FeaturePlot_PTs2.pdf"), width = 10, height = 8)
f5
dev.off()
## png 
##   2

#PT-S3 markers

f6 <- FeaturePlot(integrated, features = c("Atp11a", "Slc13a3"), 
    min.cutoff = "q9")
f6

pdf(paste0(outdir, "/13_FeaturePlot_PTs3.pdf"), width = 10, height = 8)
f6
dev.off()
## png 
##   2

#Loop of Henle

f7 <- FeaturePlot(integrated, features = c("Slc12a1", "Umod"), 
    min.cutoff = "q9")
f7

pdf(paste0(outdir, "/14_FeaturePlot_LOH.pdf"), width = 10, height = 8)
f7
dev.off()
## png 
##   2

#Distal CT

f8 <- FeaturePlot(integrated, features = c("Slc12a3", "Pvalb"), 
    min.cutoff = "q9")
f8

pdf(paste0(outdir, "/15_FeaturePlot_DCT.pdf"), width = 10, height = 8)
f8
dev.off()
## png 
##   2

#Conn Tubule

f21 <- FeaturePlot(integrated, features = c("Calb1"), min.cutoff = "q9")
f21

pdf(paste0(outdir, "/29_FeaturePlot_ConnTub.pdf"), width = 10, 
    height = 8)
f21
dev.off()
## png 
##   2

#CD PC

f9 <- FeaturePlot(integrated, features = c("Aqp2", "Hsd11b2"), 
    min.cutoff = "q9")
f9

pdf(paste0(outdir, "/16_FeaturePlot_CD-PC.pdf"), width = 10, 
    height = 8)
f9
dev.off()
## png 
##   2

#CD-IC

f10 <- FeaturePlot(integrated, features = c("Atp6v1g3", "Atp6v0d2"), 
    min.cutoff = "q9")
f10

pdf(paste0(outdir, "/17_FeaturePlot_CD-IC.pdf"), width = 10, 
    height = 8)
f10
dev.off()
## png 
##   2

#CD Trans

f11 <- FeaturePlot(integrated, features = c("Slc26a4", "Insrr", 
    "Rhbg"), min.cutoff = "q9")
f11

pdf(paste0(outdir, "/18_FeaturePlot_CD-Trans.pdf"), width = 10, 
    height = 8)
f11
dev.off()
## png 
##   2

#Fibroblast

f12 <- FeaturePlot(integrated, features = c("Plac8", "S100a4", 
    "Pdgfrb"), min.cutoff = "q9")
f12

pdf(paste0(outdir, "/19_FeaturePlot_Fib.pdf"), width = 10, height = 8)
f12
dev.off()
## png 
##   2

#Macro

f13 <- FeaturePlot(integrated, features = c("C1qa", "Cd68", "C1qb"), 
    min.cutoff = "q9")
f13

pdf(paste0(outdir, "/20_FeaturePlot_Macro.pdf"), width = 10, 
    height = 8)
f13
dev.off()
## png 
##   2

#PMN

f14 <- FeaturePlot(integrated, features = c("S100a8", "Ly6g", 
    "S100a9"), min.cutoff = "q9")
f14

pdf(paste0(outdir, "/21_FeaturePlot_PMN.pdf"), width = 10, height = 8)
f14
dev.off()
## png 
##   2

#B lymph

f15 <- FeaturePlot(integrated, features = c("Cd79a", "Cd79b", 
    "Cd19"), min.cutoff = "q9")
f15

pdf(paste0(outdir, "/22_FeaturePlot_Blymph.pdf"), width = 10, 
    height = 8)
f15
dev.off()
## png 
##   2

#Tlymph

f16 <- FeaturePlot(integrated, features = c("Ltb", "Cd4", "Cxcr6"), 
    min.cutoff = "q9")
f16

pdf(paste0(outdir, "/23_FeaturePlot_Tlymph.pdf"), width = 10, 
    height = 8)
f16
dev.off()
## png 
##   2

#NK

f17 <- FeaturePlot(integrated, features = c("Gzma", "Nkg7"), 
    min.cutoff = "q9")
f17

pdf(paste0(outdir, "/24_FeaturePlot_NK.pdf"), width = 10, height = 8)
f17
dev.off()
## png 
##   2

#Novel1

f18 <- FeaturePlot(integrated, features = c("Slc27a2", "Lrp2", 
    "Cdca3"), min.cutoff = "q9")
f18

pdf(paste0(outdir, "/25_FeaturePlot_Novel1.pdf"), width = 10, 
    height = 8)
f18
dev.off()
## png 
##   2
# library(Seurat)
DefaultAssay(integrated) <- "RNA"
clusters <- levels(integrated@active.ident)
conserved.markers <- data.frame(matrix(ncol = 14))
for (c in clusters) {
    print(c)
    markers.c <- FindConservedMarkers(integrated, ident.1 = c, 
        grouping.var = "sample.id", verbose = T)
    markers.c <- cbind(data.frame(cluster = rep(c, dim(markers.c)[1]), 
        gene = rownames(markers.c)), markers.c)
    write.table(markers.c, file = paste0(outdir, "/11_markers_", 
        c, ".txt"))
    colnames(conserved.markers) <- colnames(markers.c)
    conserved.markers <- rbind(conserved.markers, markers.c)
    head(conserved.markers)
}
## [1] "0"
## [1] "1"
## [1] "2"
## [1] "3"
## [1] "4"
## [1] "5"
## [1] "6"
## [1] "7"
## [1] "8"
## [1] "9"
## [1] "10"
## [1] "11"
## [1] "12"
## [1] "13"
## [1] "14"
conserved.markers <- conserved.markers[-1, ]
write.table(conserved.markers, file = paste0(outdir, "/12_conserved.markers.tsv"), 
    quote = T, sep = "\t", col.names = NA)
saveRDS(conserved.markers, paste0(outdir, "/12.conserved.markers.rds"))
# conserved.markers <-
# readRDS('./2_MBP-clustering/12.conserved.markers.rds')
integrated <- RenameIdents(integrated, `0` = "PT-s3", `1` = "PT-s1", 
    `2` = "PT-s2", `3` = "Endo", `4` = "CT", `5` = "PT-s3", `6` = "LOH", 
    `7` = "Fib", `8` = "DCT", `9` = "CD-PC", `10` = "Macro", 
    `11` = "CD-IC", `12` = "Lympho", `13` = "Novel", `14` = "Podo", 
    `15` = "PMN")

d2 <- DimPlot(integrated, label = TRUE, label.size = 8)
d2

pdf(paste0(outdir, "/32_Dimplot_newidents.pdf"))
d2
dev.off()
## png 
##   2
d3 <- DimPlot(integrated, group.by = "sample.id", split.by = "sample.id", 
    pt.size = 0.2, ncol = 2)
d3

pdf(paste0(outdir, "/31_DimPlot_newidents_split_by_samples.pdf"), 
    width = 16, height = 9)
d3
dev.off()
## png 
##   2

Identify cells expressing Il6

DefaultAssay(integrated) <- "RNA"
f19 <- FeaturePlot(integrated, features = "Il6", order = T, label = T, 
    label.size = 6)
f19

pdf(paste0(outdir, "/26_FeaturePlot_Il6.pdf"))
f19
dev.off()
## png 
##   2
f20 <- FeaturePlot(integrated, features = c("Il6"), split.by = "sample.id", 
    max.cutoff = 3, cols = c("grey", "red"))
f20

pdf(paste0(outdir, "/27_FeaturePlot_Il6-sham-CLP.pdf"))
f20
dev.off()
## png 
##   2
library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
integrated$celltype.stim <- paste(Idents(integrated), integrated$sample.id, 
    sep = "_")
integrated$celltype <- Idents(integrated)
Idents(integrated) <- "celltype"
plots <- VlnPlot(integrated, features = c("Il6"), split.by = "sample.id", 
    group.by = "celltype", pt.size = 0, combine = FALSE)
library(patchwork)
wrap_plots(plots = plots, ncol = 1)

Session Information

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.8 (Santiago)
## 
## Matrix products: default
## BLAS:   /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/libblas.so.3.8.0
## LAPACK: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/liblapack.so.3.8.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.3.3      cowplot_1.1.1      SeuratObject_4.0.0 Seurat_4.0.0      
## [5] patchwork_1.1.1   
## 
## loaded via a namespace (and not attached):
##   [1] nlme_3.1-152         matrixStats_0.58.0   RcppAnnoy_0.0.18    
##   [4] RColorBrewer_1.1-2   httr_1.4.2           sctransform_0.3.2   
##   [7] tools_4.0.3          R6_2.5.0             irlba_2.3.3         
##  [10] rpart_4.1-15         KernSmooth_2.23-18   uwot_0.1.10         
##  [13] mgcv_1.8-33          lazyeval_0.2.2       colorspace_2.0-0    
##  [16] withr_2.4.1          tidyselect_1.1.0     gridExtra_2.3       
##  [19] compiler_4.0.3       formatR_1.7          plotly_4.9.3        
##  [22] labeling_0.4.2       scales_1.1.1         lmtest_0.9-38       
##  [25] spatstat.data_2.0-0  ggridges_0.5.3       pbapply_1.4-3       
##  [28] spatstat_1.64-1      goftest_1.2-2        stringr_1.4.0       
##  [31] digest_0.6.27        spatstat.utils_2.0-0 rmarkdown_2.6       
##  [34] pkgconfig_2.0.3      htmltools_0.5.1.1    parallelly_1.23.0   
##  [37] limma_3.46.0         highr_0.8            fastmap_1.1.0       
##  [40] htmlwidgets_1.5.3    rlang_0.4.10         shiny_1.6.0         
##  [43] farver_2.0.3         generics_0.1.0       zoo_1.8-8           
##  [46] jsonlite_1.7.2       ica_1.0-2            dplyr_1.0.4         
##  [49] magrittr_2.0.1       Matrix_1.3-2         Rcpp_1.0.6          
##  [52] munsell_0.5.0        abind_1.4-5          reticulate_1.18     
##  [55] lifecycle_1.0.0      stringi_1.5.3        yaml_2.2.1          
##  [58] gbRd_0.4-11          MASS_7.3-53.1        Rtsne_0.15          
##  [61] plyr_1.8.6           grid_4.0.3           parallel_4.0.3      
##  [64] listenv_0.8.0        promises_1.2.0.1     ggrepel_0.9.1       
##  [67] crayon_1.4.1         deldir_0.2-9         miniUI_0.1.1.1      
##  [70] lattice_0.20-41      splines_4.0.3        tensor_1.5          
##  [73] knitr_1.31           pillar_1.4.7         igraph_1.2.6        
##  [76] future.apply_1.7.0   reshape2_1.4.4       codetools_0.2-18    
##  [79] leiden_0.3.7         glue_1.4.2           evaluate_0.14       
##  [82] metap_1.1            data.table_1.13.6    Rdpack_2.1          
##  [85] vctrs_0.3.6          png_0.1-7            httpuv_1.5.5        
##  [88] gtable_0.3.0         RANN_2.6.1           purrr_0.3.4         
##  [91] polyclip_1.10-0      tidyr_1.1.2          scattermore_0.7     
##  [94] future_1.21.0        xfun_0.20            rbibutils_2.0       
##  [97] mime_0.10            xtable_1.8-4         RSpectra_0.16-0     
## [100] later_1.1.0.1        survival_3.2-7       viridisLite_0.3.0   
## [103] tibble_3.0.6         cluster_2.1.1        globals_0.14.0      
## [106] fitdistrplus_1.1-3   ellipsis_0.3.1       ROCR_1.0-11
writeLines(capture.output(sessionInfo()), "./scripts/1_JP_scripts/2_MBP-clustering_HARDAC.sessionInfo.txt")